You Can’t Fix What You Don’t See (Campaign Analytics, not Campaign Reporting)

We all know the routine. You work hard. You do the research. Develop a strategy. You send thousands of mailers and e-mails to potential prospects. Then you close your eyes. You cross your fingers. You wait. Patiently. (Okay, sometimes not so patiently.)

Several weeks later the Key Performance Indicator report shows up on your desk. It is loaded with data. Now you’ve got Conversion Rates, Acquisition Costs, ROI, paybacks. Now what? What do all these numbers mean?

A number by itself is just a number. It is not good or bad. It only really has value when you COMPARE it to other numbers. For example, imagine yourself driving. There is no traffic. You found the perfect radio station. This ride feels good. In the middle of a steering wheel drum solo you look down. The speedometer says 85.

Unless you know the speed limit is 55, you have no reason to slow down.

You can’t really come up with your next move until you know how numbers are related. (Now stop imagining you are driving before you see the Highway Patrol gaining on you in your rearview mirror.)

The same principle applies to KPIs. That information becomes more valuable when it has context. How does this KPI compare to the KPIs set before the campaign? How do these numbers stack up against earlier campaigns? How do the numbers look against industry averages?

Now you know how well you are doing with the campaign. But do you know WHY?

You go back to the IT department and bug them for more specific information on campaign segments. “Why did we have so few prospects in New York?” Why do we have such a gender disparity?”

Every question means another report, and more waiting.

By the time the reports are designed and approved, your next campaign has already begun.

You need a more dynamic reporting solution. Remember, you can’t fix what you don’t see. A “question and answer” approach to analytics is the remedy for you. You need to see the KPIs on your dashboard, drill down to the details, and slice and dice the data to answer all your questions.

With the right solution, you should be able to click on “Response Rate,” break it down by state and zip code, find the lowest responding zip codes, get the list of non-responders, merge it with their demographics data, visualize it on a map and do it all within five minutes!